(A)ATSR RE-ANALYSIS FOR CLIMATE - CLOUD CLEARING METHODOLOGY

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1 (A)ATSR RE-ANALYSIS FOR CLIMATE - CLOUD CLEARING METHODOLOGY Chri Old, Chri Merchant Univerity of Edinburgh, The Crew Building, Wet Main Road, Edinburgh, EH9 3JN, United Kingdom cold@ed.ac.uk chri.merchant@ed.ac.uk ABSTRACT The aim of the (A)ATSR re-analyi for climate project i to derive a homogeneou record of SST covering 1991 to 2007 from the obervation of the erie of three ATSR radiometer. One feature of the exiting (A)ATSR record that could be improved i the high cloud-clearing fale alarm rate produced by the largecale patial coherence and hitogram tet. To addre thi iue, a new cheme uing Bayeian inverion to detect cloud will be applied. A ummary of the validation of the Bayeian inverion code i preented, and the application of the method to (A)ATSR imagery i dicued. A comparion i made between the cloud mak generated uing the Bayeian cheme and the operational open-ocean cloud-mak. 1. INTRODUCTION For the improved modelling and prediction of climate change procee there i a need for accurate global and regional SST meaurement. To addre thi need a 5 year project aimed at producing a 15-year SST record from the ATSR erie of intrument i currently underway, called (A)RC - (Advanced) along track canning radiometer Re-analyi for Climate. Throughout thi paper the ATSR erie of enor (ATSR, ATSR-2, AATSR) will be called (A)ATSR. Thi erie of enor provide high-reolution (~1km) full globe imagery with a contant overpa time, the meaured BT are exceptionally well calibrated, the intrument repone function are ufficiently characterized to allow accurate forward modelling of the obervation, the dual view capability allow the retrieval of SST that are robut to atmopheric condition, and there i ufficient overlap between enor in the erie to generate a long table record. For thee reaon thi data record provide a good bai for generating a quality SST record uitable for climate tudie. Te of value to the climate community the record mut pan at leat 15 year and meet the requirement for accuracy and homogeneity. The target for the (A)RC project i a tability, in the abence of real SST change, of 0.05K per decade, to allow regional and global trend of up to 0.2K per decade te quantified. Te of value, a thorough characterization of the random and ytematic error in the record i alo required. To achieve the bet global coverage in the data record, the cloud creening ued need te optimal in hit rate and minimal in fale alarm rate. The reaon for thi requirement i that the final SST product will be averaged to a pixel reolution of 1 in latitude and longitude. Failure in the cloud creening can lead to unacceptably high error (>1K) in the cell averaged SST. The (A)ATSR operational cloud creening ytem [1] currently over-creen the imagery leading to a high fale alarm rate. Therefore one of the key area of improvement aociated with the (A)RC project i the production of a more accurate cloud mak. Merchant et al. [2] propoed a Bayeian inverion cheme that reduce the fale alarm rate while maintaining a high hit rate. A generalized et of oftware tool for applying thi cloud detection cheme to any atellite TIR imagery i being developed at the Univerity of Edinburgh, UK. Thi oftware will be ued to provide a new cloud clearing product in the form a field defining the probability that a given pixel i in a clear cene. From thi field the end-uer can define a cloud mak with the everity required for their application. In thi paper the limitation of the current operational cloud clearing cheme will be identified and the advantage of the new cheme uing Bayeian in verion to detect cloud will be dicued. Reult from the validation of the oftware will be preented. The key iue that have te addre when applying the method to the (A)ATSR enor will be preented, including a decription of the error in the oberved TOA BT. Latly the reult of applying the Bayeian inverion cheme to the a ection of AATSR imagery where the operational mak ha a high fale alarm rate i dicued. 2. (A)ATSR CLOUD CLEARING ISSUES A ubtantial amount of time ha been pent developing a quality cloud mak product for the (A)ATSR enor uing traditional threhold method. Depite thi effort, there i till a relatively high fale alarm rate in the cloud mak product. It i a difficult tak to et globally Proc. Enviat Sympoium 2007, Montreux, Switzerland April 2007 (ESA SP-636, July 2007)

2 (a) Operational Mak (b) Fale RGB Image (c) Bayeian Mak Figure 1. Comparion of the operational cloud mak with the mak baed on a 0.5 threhold applied to the probability of clear-cene field generated uing the Bayeian inverion oftware. In the cloud mak grey pixel are land, white pixel are clear cene over open-ocean and black pixel are cloud over open-ocean. valid threhold value that produce an optimal cloud mak. In an attempt to reolve thi problem Zavody et al. [1] developed a ytem of dynamic threhold that ue local data to correct the threhold value. However thi ytem i not globally robut and a few of the tet fail on occaion. There are three tet within the (A)ATSR cloud clearing et that produce the majority of the fale alarm. Thee are the 11m-12m hitogram tet, the 1.6m hitogram tet (day-time imagery only) and the 11m patial coherence tet. Fig. 1a give an example of a day-time image egment from an AATSR orbit where the two hitogram tet have failed everely over the central 512x512 egment of the image. In the lower egment the creening ha been robut, but in the upper egement the 1.6m hitogram tet ha produced a high fale alarm rate. Thee large cale fale alarm occur in approximately 7% of the imagery. Thi i ignificant in term of the development of an SST product uitable for climate tudie. An attempt wa made to pot-correct for the fale alarm due to the patial coherence tet, but the enitivity of the threhold method meant that little improvement wa gained for a ignificant amount of extra computation. Thi highlight one of the main limitation of the threhold creening method, namely it enitivity to mall change. An alternative cheme that ue a Bayeian inverion to detect cloud i te applied for the (A)RC project. Thi cheme ha the advantage that it i completely adaptable to change in

3 the ytem, which i critical for the (A)ATSR erie of intrument each of which have unique characteritic that need accommodating in the cloud clearing. 3. CLOUD DETECTION USING A BAYESIAN INVERSION A cheme for detecting cloud uing a Bayeian inverion ha been developed by Merchant et al [2], and i an extenion of the work preented by Englih et al. [3] in the context of microwave obervation. The inverion take advantage of the fact that information about the prior tate of the atmophere i available in the form NWP field and climatologie. Given thi prior tate information the clear-cene TOA BT can be predicted uing a uitable radiative tranfer model (RTM). Uing Baye theorem it i poible to derive a meaure of the probability that a pixel i in a clear cene from the prior prediction of the clear cene TOA BT The probability that a pixel i viewed through a clearky i given by [2] where P P ( ) ( c ) P( y x, c ) c, x = 1 + P( c) P( y x, c) 1 y (1) o y i a vector of the oberved TOA BT and b their local tandard deviation on a 3 3 grid of pixel, x i the prior tate vector defined by the NWP and/or climatology field, c i the condition of clear-cene and c i the complementary condition of not a clear cene. The equation i further expanded by auming that the pectral obervation are independent of the cene texture, allowing the conditional probabilitie on the RHS of Eq. 1 te expanded giving and ( x, c) P( y x, c) P( y x, c) P y = (2) ( x, c ) P( y x, c ) P( y x, c ) P y = (3) where the ubcript and t define the pectral and textural component of the obervation vector. The clear-cene conditional probability for the oberved pectral component i modelled, auming a Gauian ditribution in the error in the obervation and background tate, by P ( y x, c) exp = 1 { ( ) ( ) ( )} 1 T T 2 y y H' BH' + R y y ( 2π ) n 2 T H' BH' + R t t 1 2 (4) b b where the prior vector y F( x ) = i modelled from the b b tate vector x uing the forward model F, H' = y x i the tangent linear of the forward model with repect to the tate vector, B i the error covariance of the background tate vector, and R i the combined error covariance of the obervation and the forward model. For implicity it ha been aumed that the majority of the variance in the TOA BT can be attributed to variation in the SST and TCWV, therefore only thee two field are ued to define the H' and B matrice. For a clear cene the atmopheric variability over the cale of interet for a texture meaure defined by the local tandard deviation (LSD) can be aumed negligible. Therefore the main factor contributing to the variation in LSD of the BT are the radiometric noie in the channel and the patial variability in the SST. The probability denity function (PDF) for the LSD of a et of N Gauian random variable i given by a Chi ditribution. The PDF of the LSD of the SST i le eaily defined. At preent an empirical model of thi PDF i ued. To contruct the clear cene texture PDF the PDF for the noie and SST mut be convolved. The convolution can only be done in variance pace, therefore an empirical model of SST variance ha been generated, which i convolved with the Chi-quared ditribution that define the variance of N Gauian random variable. The reulting PDF i then recaled to give the correct unit for the LSD PDF. The cloudy ky conditional PDF for the pectral and textural component are to complicated to generate and analytical form for, thu empirical ditribution are generated from creened imagery. Method are currently being developed to model the pectral cloudyky PDF, but thee will not be ued for the (A)RC project. 4. SOFTWARE VALIDATION The generalied oftware for applying the Bayeian inverion to atellite TIR imagery ha been validated againt the Météo-France interactive databae generated for Meteoat-8(v1.2) [4] and GOES-08 [5]. Thee databae conit of a large et of difficult creening cae, hand elected and categorized by expert operator (nephanalyt). Included in the databae are co-located atmopheric profile from the Météo-France ARPEGE model, imagery on a 5 5 pixel grid centred on the pixel of interet, and the cloud creening reult from the Météo-France operational creening (available for Meteoat-8 only). Each record in the databae i given a target claification by the operator to claify the central 3 3 grid of pixel. A direct comparion of the target claification with the cloud creening reult allow the generation of kill core that define the accuracy of the creening method.

4 The ARPEGE field were ued to define the prior tate vector, and the NWPSAF radiative tranfer model RTTOV-8 [6] wa ued to model the prior clear-ky TOA BT. It wa found that the low reolution urface temperature field wa not adequate a a Bayeian prior due to modification in the coatal zone SST for the operational cloud clearing. The prior SST field wa replaced with the Météo-France fine-cale 10-day SST climatology [7] with a large-cale correction to the weekly Reynold SST [8] analyi at 1 patial reolution. The correction to the Reynold SST i neceary to accommodate large deviation from the climatology, uch a in the El Nino year of To obtain the bet meaure of the prior field error, the databae record claified a clear cene were ued to etimate the error in the SST and TCWV. The SST error wa determined from the tatitic of the difference between the prior and retrieved clear-cene SST. The error in the TCWV wa etimated by propagating the error through Eq. 4 uing the previou etimate of the prior SST error, the channel NET value and the tangent linear to the RTTOV-8 model with repect to SST and TCWV. The cloud mak ued to meaure the kill of the Bayeian inverion wa generated by applying a threhold of 0.5 to the clear-cene probability field. Thi value give the optimum balance between hit rate and fale alarm. The kill core ued to meaure the cloud clearing cheme are the proportion of perfect claification (PP), the hit rate (HR), the fale alarm rate (FAR), and the true kill core (TSS = HR-FAR). The kill core generated for the Meteoat-8 interactive target databae are ummaried in Tab. 1. It i apparent the Bayeian inverion provide a high kill cloud clearing method when compared with the operational cloud clearing method. The hit rate i higher than both operational cheme, while maintaining a low fale alarm rate. For thee reaon it provide a feaible alternative for cloud clearing in the (A)RC project. Table 1. Software validation kill core comparion for the Meteoat-8 databae of interactive target. Bayeian Météo-France Met Office PP 95.3% 85.1% 88.5% HR 96.0% 80.8% 84.8% FAR 5.9% 7.8% 5.3% TSS 90.1% 73.0% 79.5% 5. APPLICATION TO (A)ATSR SERIES To apply the Bayeian inverion for cloud detection to the (A)ATSR erie of enor key parameter need te defined. Thee include the prior field, the channel noie, and the cloudy-cene PDF. The prior TOA BT will be calculated uing RTTOV-8 a thi RTM ha high quality retrieval coefficient for the (A)ATSR erie and i eaily linked with the Bayeian inverion oftware Prior State Vector To maintain conitency acro the record the ERA-40 reanalyi field will be ued to define the prior atmopheric tate. Thi record cover the operational life time of the (A)ATSR intrument. Given the high reolution of the (A)ATSR enor, the ERA-40 SST field will not have ufficient reolution to be adequate for the Bayeian inverion. The Faugere fine-cale climatology will be ued with a large cale correction to the ERA-40 SST. Thi provide enough definition to capture the larger oceanic front Quantifying Channel Noie The Bayeian inverion ha been found te enitive to the accuracy with which the channel noie i defined. Given that the calculation i baed around the TOA BT, the channel noie take the form of the noie equivalent change in temperature (NET). It wa alo noted in ection 3 that channel noie i ued to define the clear-ky textural PDF. Therefore to obtain the optimal inverion reult a decription of the variation in channel noie over the life time of the enor i required. The (A)ATSR erie ue an onboard calibration ytem to maintain the reolution in the obervation. A pinoff from thi calibration ytem i a continuou record of the channel noie in the form of meaurement of two reference black bodie of known temperature. A running analyi i carried out on the noie data for AATSR; however a imilar analyi ha not be provided for ATSR and ATSR-2. To decribe the channel noie for thee two intrument, an analyi of the UBT black body count data ha been performed. To obtain a full decription of the noie characteritic of the channel a ubet of the full UBT record wa ued. Thi conited of one egment per day randomly elected from a randomly elected orbit. The twlack bodie are oberved in every can, and there are 32 ample of radiometer count per black body. Thee black body count are converted to a radiance uing the calibration data then the variance in the black body radiance meaurement from a egment i converted to a NET value for that day. Fig. 2 preent the noie characteritic for the TIR channel for the three enor. Starting with ATSR, the main feature i the riing trend in the noie level over the lifetime of the intrument. Thi reult from a failure

5 in the cooling ytem hortly after the tart of the operational life of the intrument. The intrument gradually warmed over the miion reulting in increaed thermal noie in the enor. Alo very apparent i the failure in the 3.7m channel early in the miion. (a) ATSR The ATSR-2 intrument alo uffered an over heating problem early in the miion which left a ix month gap n the record while the problem wa being corrected from the ground. After thi gap the noie level have a large variability, but there i no overall trend in the noie level. The tability in the 11 and 12m channel improve over the econd half of the miion, but till ha hift in level that will have te accounted for. The AATSR intrument i proving te very table. There i no noticeable trend in the noie characteritic of the three channel, and there are a mall number of occaion when the noie characteritic vary ignificantly from the mean. (b) ATSR-2 A feature that i high lighted in all three enor i the difference in dependence of the channel noie on the cene temperature. It i apparent that the noie in the 11 and 12m channel ha a very weak dependence on cene temperature. However the 3.7m channel ha a very trong dependence on the cene temperature due to it enitivity in the SST thermal range. The amplitude of the 3.7m channel noie variation with temperature i uch that it will have te accounted for in the Bayeian calculation. To model the NET temperature dependence a power law ha been aumed, given by n NE T 1 T1 = A NE T T (5) 2 2 (c) AATSR Uing the data from the twlack bodie to repreent the temperature and NET value, the coefficient A and n can be determined for each channel. Uing the NET and temperature of the cold black body to define one pair of the variable the NET for any temperature T 2 can be calculated by rearranging Eq. 5. Tab. 2 give the contant calculated for the three channel on AATSR, and the correponding temperature dependence curve are plotted in Fig. 3. Table 2. Coefficient defining the NET temperature dependence for the three TIR channel on AATSR. n() A() 3.7 m m m Figure 2. The NET value calculated the UBT count product for the TIR channel on the (A)ATSR intrument. The blue point are calculated uing the radiometer count from the cold calibration black body and the red are from the warm calibration black body.

6 7. REFERENCES 1. Zavody, A.M., Mutlow, C.T. & Llewelyn-Jone, D.T. (2000). Cloud clearing over the ocean in the proceing of data from the Along-Track Scanning Radiometer (ATSR). J. Atmo. Ocean Technol., 17, Merchant, C.J., Harri, A.R., Maturi, E. & MacCallum, S. (2005). Probabilitic phyically baed cloud creening of atellite infrared imagery for operational ea urface temperature retrieval. Quart. J. Roy. Meteorol. Soc., 131, Figure 3. NET (K) temperature dependence for the thermal channel on the AATSR intrument PDF Look Up Table The pectral and textural cloudy ky PDF te ued in the (A)RC project have been defined empirically from ubet of the data record and take the form of look-uptable (LUT). Separate LUT will be generated for each intrument. At preent the operational cloud mak ha been ued to identify cloud in the imagery ubet. During the proceing of the record it hould be poible to iteratively improve thee PDF uing the new cloud mak generated from the Bayeian inverion. 6. RESULTS The Bayeian inverion wa applied to the example given in Fig. 1 where the operational threhold tet had failed. The comparion cloud mak hown in Fig. 1c wa generated uing a threhold of 0.5 on the clear-ky probability field. Comparing the Bayeian mak with the operational mak and the fale RBG image of the cene, it i clear that the Bayeian mak provide a ignificant improvement over the operational mak. One effect that reult from the ue of the texture meaure i that trong ocean front tend to get flagged a cloud. Typically only the peak of the front i flagged therefore thi hould not ignificantly affect the averaging of the retrieved SST onto a 1 grid. The effect will have a far maller impact than the current fale alarm rate produced by the operational hitogram tet. 3. Englih, S.J., Erye, J.R.., & Smith, J.A. (1999). A cloud detection cheme for ue with atellite ounding radiance in the context of data aimilation for numerical weather prediction. Quart. J. Roy. Meteorol. Soc., 125, Le Gléau, H. & Derrien, M. (2006). Validation report for the PGE of the SAFNWC/MSG v1.1. Météo-France/Centre Météorologie Spatiale, France. [Online]. Available: -NWC-IOP-MFL-SCI_VAL-01_v1.1.pdf 5. Le Gléau, H. & Derrien, M. (2000). Prototype Scientific Decription for Météo-France/CMS. SAF/NWC/MFCMS/MTR/PSD Iue 1, Rev. 1. Météo-France/Centre Météorologie Spatiale, France. [Online]. Available: ci10.pdf 6.Saunder, R., Matricardi, M. & Brunel,P. An improved fat radiative tranfer model for aimilation of atellite radiance obervation. Quart. J. Roy. Meteor. Soc., 125, Faugere, Y., Le Borgne, P. & Roquet, H. (2001). Realiation d'une climatologie mondiale de la temperature de urface de la mer a echelle fine. La Meteorologie, 35,24-35, Reynold, R.W., Rayner, N.A., Smith, T.M., Stoke, D.C. & Wang, W. (2002). An improved in itu and atellite ea urface temperature analyi for climate. J. Climate, 15, Finally, due to the early failure of the 3.7m channel on ATSR the mot homogeneou record will reult from uing only the 11 and 12m channel in the Bayeian inverion. However the more channel ued the better the inverion, therefore a econd tream will be generated where all available channel are ued in the Bayeian calculation for a given image giving a greater choice of cloud clearing product in the final record.